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An improved particle swarm optimization algorithm and its application in solving forward kinematics of a 3-DoF parallel manipulator

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第5期235卷 896-907页

作者： Zhang, Shuzhen Yuan, Xiaolong Docherty, Paul D. Yang, Kai Li, Chunling Lanzhou Univ Technol Coll Mech & Elect Engn 36 Pengjiaping Rd Lanzhou 730050 Peoples R China Univ Canterbury Dept Mech Engn Christchurch New Zealand

This paper proposes an improved particle swarm optimization to study the forward kinematic of a solar tracking device which has two rotational and one translational degree of freedom. The forward kinematics of the parallel manipulator is transformed into an optimization problem by solving the inverse kinematics equations. The proposed method combines inertial weight with the iterations number and the distance between current swarm particles and the optimum to improve convergence ability and speed. The novel cognitive and social parameters are adjusted by the inertia weight to enhance unity and intelligence of the algorithm. A stochastic mutation is used to diversify swarm for faster convergence via local optima evasion in high dimensional complex optimization problems. The performance of the proposed method is demonstrated by applying it to four benchmark functions and comparing convergence with three popular particle swarm optimization methods to verify the feasibility of the improved method. The behaviors of the proposed method using variable cognitive and social parameters and fixed value are also tested to verify fast convergence speed of variable parameters method. And further, an application example uses the method to determine the forward kinematics of a three-degree-of-freedom parallel manipulator. Finally, the mechanism simulations model of the parallel manipulator are carefully built and analyzed to verify the correctness of the proposed algorithm in PTC Creo Parametric software. In all cases tested, the proposed algorithm achieved much faster convergence and either improved or proximal fitness values.

关键词： Parallel manipulator forward kinematics improved particle swarm algorithm inertia weight diversify swarm

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Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on improved particle swarm Optimization algorithm

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Energy Engineering 2024年第6期121卷 1535-1555页

作者： Huanan Yu Hangyu Li He Wang Shiqiang Li Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization ***, a measurement point distribution optimization model is formulated, leveraging compressive *** model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

关键词： Optimal allocation improved particle swarm algorithm fault location compressed sensing DC distribution network

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Optimal Operation of Hydropower Station Based on improved particle swarm Optimization algorithm

Optimal Operation of Hydropower Station Based on Improved Pa...

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International Conference on Advanced Research on Computer Science and Information Engineering

作者： Ma, Xin N China Univ Water Conservancy & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Province Peoples R China

ISBN: (纸本)9783642214103

Hydropower station optimal operation is a complex nonlinear combinatorial optimization problem. An improved particle swarm optimization (IPSO) algorithm is suggested. The culture algorithm is introduced and local random search operator to achieve knowledge structure in belief space and enhance the population diversity and increase the capacity of global search with the introduction of culture algorithm, The simulation results of new algorithm compares with particle swarm optimization (PSO) algorithm and shows that this new algorithm can overcome the shortcomings of the traditional PSO and to gain better convergence speed and computational accuracy.

关键词： Hydropower station optimal operation nonlinear problem improved particle swarm algorithm

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Multi-Objective Comprehensive Charging/Discharging Scheduling Strategy for Electric Vehicles Based on the improved particle swarm Optimization algorithm

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Fang, Baling Li, Bo Li, Xingcheng Jia, Yunzhen Xu, Wenzhe Liao, Ying Hunan Univ Technol Coll Elect & Informat Engn Zhuzhou Peoples R China Hunan Creator Informat Technol Co Ltd Changsha Peoples R China

To solve the problems that a large number of random and uncontrolled electric vehicles (EVs) connecting to the distribution network, resulting in a decrease in the performance and stability of the grid and high user costs, in this study, a multi-objective comprehensive charging/discharging scheduling strategy for EVs based on improved particle swarm optimization (IPSO) is proposed. In the distribution network, the minimum root-mean-square error and the minimum peak valley difference of system load are first designed as objective functions;on the user side, the lowest charge and discharge cost of electric vehicle users and the lowest battery loss cost are used as objective functions, then a multi-objective optimization scheduling model for EVs is established, and finally, the optimization through IPSO is performed. The simulation results show that the proposed method is effective, which enhances the peak regulating capacity of the power grid, and it optimizes the system load and reduces the user cost compared with the conventional methods.

关键词： electric vehicle multi-objective optimization improved particle swarm algorithm grid peak shaving charging discharging scheduling

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improved particle swarm optimization algorithm for enhanced coupling of coaxial optical communication laser

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OPTICAL FIBER TECHNOLOGY 2021年 64卷 102559-102559页

作者： Duan, Lian Zhou, Haibo Tan, Shuaixia Duan, Ji-an Liu, Zhixian Cent South Univ State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Zhuzhou Times New Mat Technol CO LTD Zhuzhou 412007 Hunan Peoples R China

In this paper, we investigate a novel improved particle swarm Optimization (IPSO) algorithm to enhance the coupling precision between a laser diode and a single-mode fiber. It mainly relies on the nonlinear adjustment of inertia weight. Several variations of PSO are used as references. The simulated and experimental results show that the proposed strategy has an excellent search success rate with few iterations in multi-degree of freedom alignment. This is largely due to the improvement of exploration via the adjustment of inertia weight. These results confirm that the improved PSO algorithm can be applied reliably to improve both the efficiency and accuracy in laser-fiber coupling.

关键词： Coupling alignment improved particle swarm algorithm Multi-degree of freedom alignment Inertia weight

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Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

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MARINE POLLUTION BULLETIN 2024年第Pt B期209卷 117214页

作者： Guo, Jianqun Jiang, Zhonglian Ying, Jianglong Feng, Xuejun Zheng, Fengfan Wuhan Univ Technol State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Wuhan Univ Technol Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Peoples R China Hohai Univ Inst Maritime Logist & Green Dev Nanjing 210098 Peoples R China China Classificat Soc Wuhan Rules & Res Inst Wuhan 430022 Peoples R China

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Delta) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Delta = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

关键词： Emergency resource allocation Multi-objective optimization improved particle swarm algorithm NSGA-II Entropy-weighted TOPSIS

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Optimization of Emergency Shelter Siting and Material Distribution Considering Multiple Psychological Perceptions of Disaster Victims Under Two-Dimensional Fairness Measurement

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TRANSPORTATION RESEARCH RECORD 2025年

作者： Mao, Qinghua Ma, Hao Yanshan Univ Sch Econ & Management Qinhuangdao Peoples R China

In the early stages of large-scale disasters, governments are tasked with temporary relocation of disaster victims and distribution of essential supplies. During this period, delays in emergency resource allocation and uneven distribution among shelters can lead to negative psychological impacts among disaster victims, thus affecting post-disaster rescue efficiency and psychological counseling efforts. To address this issue, this study constructs a cost function to measure the perceived suffering of disaster victims. From a two-dimensional (2-D) fairness perspective, it also formulates a comparison penalty cost function to quantify the envy effect arising from unequal distribution. In consideration of minimizing the overall social cost of rescue operations, the study develops an integrated optimization model for evacuation, shelter site selection, and material allocation that balances efficiency and fairness. Furthermore, an improved particle swarm optimization algorithm incorporating Taboo search (TSPSO) is designed for solution purposes. The model and algorithm are applied to a real case study during Typhoon "Doksuri" in 2023, which struck Quanzhou City, Fujian Province, along with four different randomly generated scenarios of varying scales. Comparative analysis against two classical algorithms is conducted. Case studies demonstrate that utilizing fairness coefficients and perceived suffering cost functions can achieve more equitable and effective humanitarian rescue operations. The results validate the effectiveness and feasibility of the model and algorithm, showing that TSPSO outperforms the other two classical algorithms.

关键词： emergency evacuation psychological perception emergency material deployment shelter site selection improved particle swarm algorithm

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Multi-parameter optimization of NPP simulation models using enhanced particle swarm method

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PROGRESS IN NUCLEAR ENERGY 2025年 184卷

作者： Li, Zikang Wang, Hang Fei, Li Peng, Minjun Xian, Zhang Zhou, Gui Harbin Engn Univ Key Subject Lab Nucl Safety & Simulat Technol Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Nucl Safety & Adv Nucl Energy Technol Harbin 150001 Peoples R China China Nucl Power Operat Technol Corp Ltd Wuhan 430000 Peoples R China

This paper delves into the optimization of simulation models for large-scale complex dynamic systems that couple multiple disciplines such as nuclear physics, heat transfer, and fluid mechanics, within the context of digital transformation in nuclear power. An enhanced particle swarm optimization (PSO) algorithm-based multi- parameter optimization method is proposed. This method integrates various strategies to improve the simulation accuracy of system-level models in replicating the operational characteristics of real systems. The effectiveness of this method is demonstrated through experiments on simulation models of the reactor coolant system and the chemical and volume control system within a full-range simulator. Post-optimization, the errors of key parameters are reduced to within 2%. This approach not only aids researchers in refining parameter design during the model development phase but also enables automatic parameter adjustments based on the actual system status after deployment. It meets the needs for online optimization and rapid tracking of actual system states in the application of nuclear power digital twin models.

关键词： Nuclear power plant simulation model improved particle swarm algorithm Parameter optimization System-level optimization Digital twin

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Artificial Intelligence Based Spacecraft Resilience Optimization in Space Informatics Digital Twins

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 1834-1847页

作者： Lyu, Zhihan Guo, Jinkang Lou, Ranran Lv, Haibin Uppsala Univ Fac Arts S-75310 Uppsala Sweden Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Minist Nat Resources North China Sea Offshore Engn Survey Inst North Sea Bur Qingdao 266100 Peoples R China

This article focuses on optimizing the elasticity of spacecraft by harnessing the power of artificial intelligence (AI) technology. With the support of spatial informatics and digital twins technology, this work initially employs AI techniques, specifically the radial basis function neural networks, a deep learning algorithm, to perform global optimization and orbit fitting for spacecraft. Augmented Lagrangian multipliers are then introduced to locally optimize this neural network. Additionally, to further enhance the spacecraft's flexibility, an improved particle swarm optimization (PSO) algorithm is applied to optimize the proposed network. The work also introduces a periodic variational multiobjective quantum particle swarm optimization (PMQPSO) algorithm. Subsequently, a rigid-flexible coupled dynamics model for the spacecraft is established, and relevant simulations and experiments are conducted to support this work. The results indicate that the average fitness of the improved PMQPSO algorithm decreases to 18.23 after 500 iterations, with its performance being at least 3.2% higher than that of the classical quantum PSO algorithm. Furthermore, after the initial decline in the first order, the limiter residuals no longer decline and exhibit convergence, as the residual curve transitions from high to low, indicating a gradual improvement in convergence and stability. These findings highlight the advantages of the PMQPSO algorithm in optimizing the spacecraft's elasticity. In conclusion, this parameter optimization holds practical significance for the design optimization of aircraft aerodynamic shapes.

关键词： Optimization Space vehicles Trajectory Aerodynamics Heuristic algorithms Neural networks Elasticity Artificial intelligence (AI) digital twins improved particle swarm algorithm spacecraft optimization spatial information

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A dynamic multi-objective evolutionary algorithm based on genetic engineering and improved particle swarm prediction strategy

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INFORMATION SCIENCES 2024年 660卷

作者： Yang, Yue Ma, Yongjie Zhao, Yan Zhang, Wenping Wang, Yu Northwest Normal Univ Coll Phys & Elect Engn Lan Zhou Peoples R China

Evolutionary and particle swarm optimisation (PSO) algorithms are widely used to solve various optimisation problems. However, both methods have limitations when applied to dynamic multi -objective optimisation problems. Therefore, we propose a dynamic multi -objective evolutionary algorithm based on genetic engineering and improved particle swarm prediction. Genetic engineering encompasses gene sequencing and editing, whereas improved particle swarm prediction involves enhancing information utilisation and dynamic performance. The improved PSO can be better combined with dynamic multi -objective optimization problems (DMOPs) for predicting changing populations, which we call improved particle swarm prediction. Gene sequencing clarifies the functions of different genes during the evolution process, and gene editing enables the algorithm to effectively avoid local optima. Expanding on this foundation, we redefined the notions of particle and global bests in the PSO strategy to improve the information utilisation. Additionally, we innovatively introduced the concept of acceleration, which greatly enhances the dynamic performance of the PSO. Finally, we conducted two experiments to verify the accuracy of our gene sequencing strategy and the performance of our optimisation algorithm. The results indicate that gene sequencing can effectively classify genes, and the developed optimisation algorithm significantly outperforms similar, state-of-the-art, and classical algorithms in terms of distribution and dynamics on 25 test problems.

关键词： Dynamic multi-objective optimization Genetic engineering improved particle swarm algorithm Prediction strategy

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